Annals of Telecommunications最新文献

The attention mechanism enables the processing of the data more efficiently by driving the neural networks to focus on the pertinent information. The increase in performance pushed their wide adoption, including for bio-signal. Multiple researchers explored their use of electroencephalography in many scenarios, including motor imagery. Despite the myriad of implementations, their achievement varies from one subject to another since the signals are delicate. In this paper, we extend our previous research (Riyad and Adib 2024) by suggesting a new implementation. The proposal employs the Convolutional Block Attention Module as a backbone with a few modifications adjusted for the nature of the electroencephalography. It uses three levels of attention that are performed on the channel, time, and electrode individually known as Channel Attention Module (CAM), Time Attention Module (TAM), and Electrode Attention Module (EAM). The compartmentalization authorizes the placing of the attention sub-block in diverse configurations, each with a specific order that impacts the extraction of the feature. Also, we suggest studying them with two structures, one with an early spatial filtering that uses the new block once and a late spatial filtering that uses the attention twice. For the experiments, we test on the dataset 2b of the BCI competition IV. The results show that placing the CAM first and feeding its output to the TAM and EAM boost the performance drastically. For optimal results, it is necessary to use the new attention once at the beginning of the network. Also, it permits an even classification of the classes compared with the others.

This paper considers the performance of an uplink cell-free massive multiple-input multiple-output (mMIMO) system using low-resolution analog-to-digital converters (ADCs) under spatially correlated Rayleigh fading channels. Taking the advantage of the spectral efficiency (SE), we consider zero-forcing (ZF) group successive interference cancellation (GSIC) detectors to a cell-free mMIMO system and treat the system with ZF and ZF-SIC detectors as special cases. Using the additive quantization noise model (AQNM), we analyze the achievable SE under both perfect and imperfect channel state information (CSI) and compare these results with systems employing high-resolution ADCs. Then, we derive closed form expressions of SE for the cell-free mMIMO system. Numerical results verify the tightness of approximation expressions, and the SE of the system with GSIC detectors can be further improved with an increased number of groups. To investigate the potential of low-resolution ADC architectures, we also study the energy efficiency (EE) of the system. Our findings indicate that employing SIC detectors significantly enhances EE relative to systems utilizing linear ZF detectors. Besides, a similar EE of the system employing SIC detectors can be achieved by the system with GSIC detectors.

The use of directional modulation is one of the prominent and practical solutions for ensuring physical layer security in modern telecommunications systems. In this method, the message signal is modulated by an array of antennas and transmitted in a specific direction toward the legitimate receiver, in such a way that the signal is degraded and not receivable correctly by eavesdroppers in other directions. By employing directional modulation based on a random frequency diverse array, secure communication can be achieved in two-dimensional space, including angle and distance, for the legitimate receiver. The secrecy performance of directional modulation based on a random frequency diverse array is considerably dependent on the information available to the transmitter side about the receiver’s location. In wireless cellular networks where the receiver’s location may change at any moment, there will be inherent errors in estimating the receiver’s location. The occurrence of errors in estimating the angle and distance between the legitimate receiver and the transmitter leads to a significant reduction in the system’s secrecy rate. In this article, a new solution is proposed to increase the robustness against errors in estimating the legitimate receiver’s location by an optimization process based on the minimum mean square error criterion. This solution leads to the improvement of the physical layer security by employing random frequency diverse array directional modulation in the presence of estimation errors. Simulation results indicate an enhancement in the secrecy rate performance of the physical layer in wireless networks.

While speech emotion recognition has traditionally focused on classifying emotions into discrete categories like happy or angry, recent research has shifted towards a dimensional approach using the Valence-Arousal-Dominance model. This model captures the continuous emotional state. However, research in speech emotion recognition (SER) consistently shows lower performance in predicting valence compared to arousal and dominance. To improve performance, we propose a system that combines acoustic and linguistic information. This work explores a novel multimodal approach for emotion recognition that combines speech and text data. This fusion strategy aims to outperform the traditional single-modality systems. Both early and late fusion techniques are investigated in this paper. Our findings show that combining modalities in a late fusion approach enhances system performance. In this late fusion architecture, the outputs from the acoustic deep learning network and the linguistic network are fed into two stacked dense neural network (NN) layers to predict valence, arousal, and dominance as continuous values. This approach leads to a significant improvement in overall emotion recognition performance compared to prior methods.

A digital identity is a digital representation of an individual’s identity. In the era of the Internet, it becomes increasingly important to adopt a digital identity format that is cryptographically secure and privacy-preserving. Indeed, a digital identity has the potential to offer better privacy protection than a physical identity document, where users are forced to disclose more information when presenting their identity documents than what is actually needed. Nevertheless, digital identities have not come without their own set of new challenges. One of the key issues to fully adopting digital identities is the lack of standards—both for the format of digital identities and the protocols for transferring them. This paper discusses the various formats and protocols currently used in the digital identity market. Then it introduces the Simple Universal Verifier (SUV) as an approach to tackle the current lack of interoperability for the verification phase. The SUV has been implemented and used to integrate a Relying Party (RP) with Microsoft and Identiproof Verifiable Credentials platforms and also integrates with the TRAIN trust management infrastructure.

The Internet of Things (IoT) enables 5G communication, and it is widely used in many industries owing to its ability to connect a massive number of devices, process data intelligently, and enable remote control. However, the open nature of wireless communications makes information security and privacy critical concerns. In this paper, we investigate the issue of physical layer security (PLS) within a two-hop wireless cooperative network, where communication is facilitated by an untrusted relay. This investigation was performed over mixed Rayleigh-Nakagami-(varvec{m}) fading channels in the presence of multiple non-colluding eavesdroppers. To improve the system’s security, we considered the incorporation of cooperative jamming and opportunistic relaying techniques. We formulated explicit equations for both the secrecy capacity and the secrecy outage probability within the system. Finally, by leveraging numerical results, simulations, and comparisons with existing works, we demonstrated the effectiveness of the proposed approach. This demonstration highlights that the suggested method serves as an effective method to enhance the security of IoT data collection processes, particularly in scenarios involving untrustworthy relays and highly risky eavesdropping threats.

In this article, we highlight the carbon footprint of data transport on Internet infrastructures by bringing together a series of studies that break down the carbon impact of digital technologies and detail the geography and routing of the Internet. The article also draws a comparison between three types of digital infrastructure where digital services are hosted either in the cloud, at the edge, or in an Internet of Edges. This comparison emphasizes the advantages of establishing shorter data routes to reduce the carbon footprint of digital communications.

In forthcoming 6 G wireless networks, spatial modulation (SM) has established itself as a technique in massive MIMO setups. In this letter, a novel SM architecture embedding linear frequency modulation (LFM) principle is proposed. In the resulting SM-LFM scheme, the data block is split into the signal and the antenna index parts, and prior to its transmission, distinctly assigns a different LFM chirp (up or down) to be mixed up with the SM block, depending on the index of the antenna to be activated. When the antenna index is even, the associated chirp is up, while the down chirp is retained otherwise. Similarly, the proposed SM-LFM receiver part consists of two main parts: the LFM chirp recognizer and the tuned SM detector. In the former, the received signal is fed to the two up and down LFM chirp-correlators, and the chirp selected at the transmitter is retrieved by an energy comparator. The target behind the incorporation of the LFM chirps, between each adjacent antennas-pair, is to mitigate the miss-estimation of the antenna indices at the receiver side. Depending upon the detected chirp, in the tuned SM detector, only the signals (indices and symbols) emanating from even- or odd-indexed antennas are processed. The performance of the resulting scheme is investigated by adopting a channel model, which considers the effects of estimation errors and antenna correlation. Two modified detectors which adapt to the LFM signaling are investigated, namely the iterative maximum ratio combining (i-MRC) and the maximum likelihood (ML) detectors. The improvement provided, by adopting LFM concept in SM, is shown in terms of data reliability and complexity reduction.